from copy import deepcopy
import math
import os, sys
import random
import traceback

from tqdm import tqdm
now_dir = os.getcwd()
sys.path.append(now_dir)
import ffmpeg
import os
from typing import Generator, List, Union
import numpy as np
import torch
import torch.nn.functional as F
import yaml
from transformers import AutoModelForMaskedLM, AutoTokenizer

from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from feature_extractor.cnhubert import CNHubert
from module.models import SynthesizerTrn
import librosa
from time import time as ttime
from tools.i18n.i18n import I18nAuto
from my_utils import load_audio
from module.mel_processing import spectrogram_torch
from TTS_infer_pack.text_segmentation_method import splits
from TTS_infer_pack.TextPreprocessor import TextPreprocessor
i18n = I18nAuto()

# configs/tts_infer.yaml
"""
default:
  device: cpu
  is_half: false
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
  vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
  flash_attn_enabled: true
  
custom:
  device: cuda
  is_half: true
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
  vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
  flash_attn_enabled: true


"""

def set_seed(seed:int):
    seed = int(seed)
    seed = seed if seed != -1 else random.randrange(1 << 32)
    print(f"Set seed to {seed}")
    os.environ['PYTHONHASHSEED'] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    try:
        if torch.cuda.is_available():
            torch.cuda.manual_seed(seed)
            torch.cuda.manual_seed_all(seed)
            # torch.backends.cudnn.deterministic = True
            # torch.backends.cudnn.benchmark = False
            # torch.backends.cudnn.enabled = True
    except:
        pass
    return seed
    
class TTS_Config:
    default_configs={
                "device": "cpu",
                "is_half": False,
                "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
                "vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
                "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
                "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
                "flash_attn_enabled": True
            }
    configs:dict = None
    def __init__(self, configs: Union[dict, str]=None):
        
        # 设置默认配置文件路径
        configs_base_path:str = "GPT_SoVITS/configs/"
        os.makedirs(configs_base_path, exist_ok=True)
        self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml")
        
        if configs in ["", None]:
            if not os.path.exists(self.configs_path):
                self.save_configs()
                print(f"Create default config file at {self.configs_path}")
            configs:dict = {"default": deepcopy(self.default_configs)}
        
        if isinstance(configs, str):
            self.configs_path = configs
            configs:dict = self._load_configs(self.configs_path)
            
        assert isinstance(configs, dict)
        default_configs:dict = configs.get("default", None)
        if default_configs is not None:
            self.default_configs = default_configs
            
        self.configs:dict = configs.get("custom", deepcopy(self.default_configs))
        
        
        self.device = self.configs.get("device", torch.device("cpu"))
        self.is_half = self.configs.get("is_half", False)
        self.flash_attn_enabled = self.configs.get("flash_attn_enabled", True)
        self.t2s_weights_path = self.configs.get("t2s_weights_path", None)
        self.vits_weights_path = self.configs.get("vits_weights_path", None)
        self.bert_base_path = self.configs.get("bert_base_path", None)
        self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None)

        
        if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)):
            self.t2s_weights_path = self.default_configs['t2s_weights_path']
            print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}")
        if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)):
            self.vits_weights_path = self.default_configs['vits_weights_path']
            print(f"fall back to default vits_weights_path: {self.vits_weights_path}")
        if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)):
            self.bert_base_path = self.default_configs['bert_base_path']
            print(f"fall back to default bert_base_path: {self.bert_base_path}")
        if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)):
            self.cnhuhbert_base_path = self.default_configs['cnhuhbert_base_path']
            print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}")
        self.update_configs()
        
        
        self.max_sec = None
        self.hz:int = 50
        self.semantic_frame_rate:str = "25hz"
        self.segment_size:int = 20480
        self.filter_length:int = 2048
        self.sampling_rate:int = 32000
        self.hop_length:int = 640
        self.win_length:int = 2048
        self.n_speakers:int = 300
        
        self.langauges:list = ["auto", "en", "zh", "ja",  "all_zh", "all_ja"]
        # print(self)
            
    def _load_configs(self, configs_path: str)->dict:
        with open(configs_path, 'r') as f:
            configs = yaml.load(f, Loader=yaml.FullLoader)
    
        return configs

    def save_configs(self, configs_path:str=None)->None:
        configs={
            "default":self.default_configs,
        }
        if self.configs is not None:
            configs["custom"] = self.update_configs()
            
        if configs_path is None:
            configs_path = self.configs_path
        with open(configs_path, 'w') as f:
            yaml.dump(configs, f)

    def update_configs(self):
        self.config = {
            "device"             : str(self.device),
            "is_half"            : self.is_half,
            "t2s_weights_path"   : self.t2s_weights_path,
            "vits_weights_path"  : self.vits_weights_path,
            "bert_base_path"     : self.bert_base_path,
            "cnhuhbert_base_path": self.cnhuhbert_base_path,
            "flash_attn_enabled" : self.flash_attn_enabled
        }
        return self.config
            
    def __str__(self):
        self.configs = self.update_configs()
        string = "TTS Config".center(100, '-') + '\n'
        for k, v in self.configs.items():
            string += f"{str(k).ljust(20)}: {str(v)}\n"
        string += "-" * 100 + '\n'
        return string
    
    def __repr__(self):
        return self.__str__()


class TTS:
    def __init__(self, configs: Union[dict, str, TTS_Config]):
        if isinstance(configs, TTS_Config):
            self.configs = configs
        else:
            self.configs:TTS_Config = TTS_Config(configs)
        
        self.t2s_model:Text2SemanticLightningModule = None
        self.vits_model:SynthesizerTrn = None
        self.bert_tokenizer:AutoTokenizer = None
        self.bert_model:AutoModelForMaskedLM = None
        self.cnhuhbert_model:CNHubert = None
        
        self._init_models()
        
        self.text_preprocessor:TextPreprocessor = \
                            TextPreprocessor(self.bert_model, 
                                            self.bert_tokenizer, 
                                            self.configs.device)
        
        
        self.prompt_cache:dict = {
            "ref_audio_path":None,
            "prompt_semantic":None,
            "refer_spepc":None,
            "prompt_text":None,
            "prompt_lang":None,
            "phones":None,
            "bert_features":None,
            "norm_text":None,
        }
        
        
        self.stop_flag:bool = False
        self.precison:torch.dtype = torch.float16 if self.configs.is_half else torch.float32

    def _init_models(self,):
        self.init_t2s_weights(self.configs.t2s_weights_path)
        self.init_vits_weights(self.configs.vits_weights_path)
        self.init_bert_weights(self.configs.bert_base_path)
        self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
        # self.enable_half_precision(self.configs.is_half)
        
        
        
    def init_cnhuhbert_weights(self, base_path: str):
        print(f"Loading CNHuBERT weights from {base_path}")
        self.cnhuhbert_model = CNHubert(base_path)
        self.cnhuhbert_model=self.cnhuhbert_model.eval()
        self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
        if self.configs.is_half and str(self.configs.device)!="cpu":
            self.cnhuhbert_model = self.cnhuhbert_model.half()
        
        
        
    def init_bert_weights(self, base_path: str):
        print(f"Loading BERT weights from {base_path}")
        self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
        self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
        self.bert_model=self.bert_model.eval()
        self.bert_model = self.bert_model.to(self.configs.device)
        if self.configs.is_half and str(self.configs.device)!="cpu":
            self.bert_model = self.bert_model.half()
        
        

    def init_vits_weights(self, weights_path: str):
        print(f"Loading VITS weights from {weights_path}")
        self.configs.vits_weights_path = weights_path
        self.configs.save_configs()
        dict_s2 = torch.load(weights_path, map_location=self.configs.device)
        hps = dict_s2["config"]
        self.configs.filter_length = hps["data"]["filter_length"]
        self.configs.segment_size = hps["train"]["segment_size"]
        self.configs.sampling_rate = hps["data"]["sampling_rate"]       
        self.configs.hop_length = hps["data"]["hop_length"]
        self.configs.win_length = hps["data"]["win_length"]
        self.configs.n_speakers = hps["data"]["n_speakers"]
        self.configs.semantic_frame_rate = "25hz"
        kwargs = hps["model"]
        vits_model = SynthesizerTrn(
            self.configs.filter_length // 2 + 1,
            self.configs.segment_size // self.configs.hop_length,
            n_speakers=self.configs.n_speakers,
            **kwargs
        )
        # if ("pretrained" not in weights_path):
        if hasattr(vits_model, "enc_q"):
            del vits_model.enc_q
            
        vits_model = vits_model.to(self.configs.device)
        vits_model = vits_model.eval()
        vits_model.load_state_dict(dict_s2["weight"], strict=False)
        self.vits_model = vits_model
        if self.configs.is_half and str(self.configs.device)!="cpu":
            self.vits_model = self.vits_model.half()

        
    def init_t2s_weights(self, weights_path: str):
        print(f"Loading Text2Semantic weights from {weights_path}")
        self.configs.t2s_weights_path = weights_path
        self.configs.save_configs()
        self.configs.hz = 50
        dict_s1 = torch.load(weights_path, map_location=self.configs.device)
        config = dict_s1["config"]
        self.configs.max_sec = config["data"]["max_sec"]
        t2s_model = Text2SemanticLightningModule(config, "****", is_train=False, 
                                                 flash_attn_enabled=self.configs.flash_attn_enabled)
        t2s_model.load_state_dict(dict_s1["weight"])
        t2s_model = t2s_model.to(self.configs.device)
        t2s_model = t2s_model.eval()
        self.t2s_model = t2s_model
        if self.configs.is_half and str(self.configs.device)!="cpu":
            self.t2s_model = self.t2s_model.half()
        
    def enable_half_precision(self, enable: bool = True):
        '''
            To enable half precision for the TTS model.
            Args:
                enable: bool, whether to enable half precision.
                
        '''
        if str(self.configs.device) == "cpu" and enable:
            print("Half precision is not supported on CPU.")
            return
        
        self.configs.is_half = enable
        self.precison = torch.float16 if enable else torch.float32
        self.configs.save_configs()
        if enable:
            if self.t2s_model is not None:
                self.t2s_model =self.t2s_model.half()
            if self.vits_model is not None:
                self.vits_model = self.vits_model.half()
            if self.bert_model is not None:
                self.bert_model =self.bert_model.half()
            if self.cnhuhbert_model is not None:
                self.cnhuhbert_model = self.cnhuhbert_model.half()
        else:
            if self.t2s_model is not None:
                self.t2s_model = self.t2s_model.float()
            if self.vits_model is not None:
                self.vits_model = self.vits_model.float()
            if self.bert_model is not None:
                self.bert_model = self.bert_model.float()
            if self.cnhuhbert_model is not None:
                self.cnhuhbert_model = self.cnhuhbert_model.float()
                
    def set_device(self, device: torch.device):
        '''
            To set the device for all models.
            Args:
                device: torch.device, the device to use for all models.
        '''
        self.configs.device = device
        self.configs.save_configs()
        if self.t2s_model is not None:
            self.t2s_model = self.t2s_model.to(device)
        if self.vits_model is not None:
            self.vits_model = self.vits_model.to(device)
        if self.bert_model is not None:
            self.bert_model = self.bert_model.to(device)
        if self.cnhuhbert_model is not None:
            self.cnhuhbert_model = self.cnhuhbert_model.to(device)
        
    def set_ref_audio(self, ref_audio_path:str):
        '''
            To set the reference audio for the TTS model, 
                including the prompt_semantic and refer_spepc.
            Args:
                ref_audio_path: str, the path of the reference audio.
        '''
        self._set_prompt_semantic(ref_audio_path)
        self._set_ref_spepc(ref_audio_path)
        
    def _set_ref_spepc(self, ref_audio_path):
        audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
        audio = torch.FloatTensor(audio)
        audio_norm = audio
        audio_norm = audio_norm.unsqueeze(0)
        spec = spectrogram_torch(
            audio_norm,
            self.configs.filter_length,
            self.configs.sampling_rate,
            self.configs.hop_length,
            self.configs.win_length,
            center=False,
        )
        spec = spec.to(self.configs.device)
        if self.configs.is_half:
            spec = spec.half()
        # self.refer_spepc = spec
        self.prompt_cache["refer_spepc"] = spec
        
        
    def _set_prompt_semantic(self, ref_wav_path:str):
        zero_wav = np.zeros(
            int(self.configs.sampling_rate * 0.3),
            dtype=np.float16 if self.configs.is_half else np.float32,
        )
        with torch.no_grad():
            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
            wav16k = torch.from_numpy(wav16k)
            zero_wav_torch = torch.from_numpy(zero_wav)
            wav16k = wav16k.to(self.configs.device)
            zero_wav_torch = zero_wav_torch.to(self.configs.device)
            if self.configs.is_half:
                wav16k = wav16k.half()
                zero_wav_torch = zero_wav_torch.half()

            wav16k = torch.cat([wav16k, zero_wav_torch])
            hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[
                "last_hidden_state"
            ].transpose(
                1, 2
            )  # .float()
            codes = self.vits_model.extract_latent(hubert_feature)
    
            prompt_semantic = codes[0, 0].to(self.configs.device)
            self.prompt_cache["prompt_semantic"] = prompt_semantic
    
    def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
        seq = sequences[0]
        ndim = seq.dim()
        if axis < 0:
            axis += ndim
        dtype:torch.dtype = seq.dtype
        pad_value = torch.tensor(pad_value, dtype=dtype)
        seq_lengths = [seq.shape[axis] for seq in sequences]
        if max_length is None:
            max_length = max(seq_lengths)
        else:
            max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length

        padded_sequences = []
        for seq, length in zip(sequences, seq_lengths):
            padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
            padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value)
            padded_sequences.append(padded_seq)
        batch = torch.stack(padded_sequences)
        return batch
    
    def to_batch(self, data:list, 
                 prompt_data:dict=None, 
                 batch_size:int=5, 
                 threshold:float=0.75, 
                 split_bucket:bool=True, 
                 device:torch.device=torch.device("cpu"),
                 precison:torch.dtype=torch.float32,
                 ):
        
        _data:list = []
        index_and_len_list = []
        for idx, item in enumerate(data):
            norm_text_len = len(item["norm_text"])
            index_and_len_list.append([idx, norm_text_len])

        batch_index_list = []
        if split_bucket:
            index_and_len_list.sort(key=lambda x: x[1])
            index_and_len_list = np.array(index_and_len_list, dtype=np.int64)            
            
            batch_index_list_len = 0
            pos = 0
            while pos <index_and_len_list.shape[0]:
                # batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
                pos_end = min(pos+batch_size,index_and_len_list.shape[0])
                while pos < pos_end:
                    batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
                    score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8)
                    if (score>=threshold) or (pos_end-pos==1):
                        batch_index=index_and_len_list[pos:pos_end, 0].tolist()
                        batch_index_list_len += len(batch_index)
                        batch_index_list.append(batch_index)
                        pos = pos_end
                        break
                    pos_end=pos_end-1
            
            assert batch_index_list_len == len(data)
            
        else:
            for i in range(len(data)):
                if i%batch_size == 0:
                    batch_index_list.append([])
                batch_index_list[-1].append(i)

                
        for batch_idx, index_list in enumerate(batch_index_list):
            item_list = [data[idx] for idx in index_list]
            phones_list = []
            phones_len_list = []
            # bert_features_list = []
            all_phones_list = []
            all_phones_len_list = []
            all_bert_features_list = []
            norm_text_batch = []
            bert_max_len = 0
            phones_max_len = 0
            for item in item_list:
                if prompt_data is not None:
                    all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
                                                .to(dtype=precison, device=device)
                    all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]).to(device)
                    phones = torch.LongTensor(item["phones"]).to(device)
                    # norm_text = prompt_data["norm_text"]+item["norm_text"]
                else:
                    all_bert_features = item["bert_features"]\
                                            .to(dtype=precison, device=device)
                    phones = torch.LongTensor(item["phones"]).to(device)
                    all_phones = phones
                    # norm_text = item["norm_text"]

                bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
                phones_max_len = max(phones_max_len, phones.shape[-1])
                
                phones_list.append(phones)
                phones_len_list.append(phones.shape[-1])
                all_phones_list.append(all_phones)
                all_phones_len_list.append(all_phones.shape[-1])
                all_bert_features_list.append(all_bert_features)
                norm_text_batch.append(item["norm_text"])
                
            phones_batch = phones_list
            all_phones_batch = all_phones_list
            all_bert_features_batch = all_bert_features_list
            
            
            # max_len = max(bert_max_len, phones_max_len)
            # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
            #### 直接对phones和bert_features进行pad，会增大复读概率。
            # all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
            # all_bert_features_batch = all_bert_features_list
            # all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=precison, device=device)
            # for idx, item in enumerate(all_bert_features_list):
            #     all_bert_features_batch[idx, :, : item.shape[-1]] = item
            
            # #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
            # all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list]
            # all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list]
            # all_phones_batch = torch.stack(all_phones_list, dim=0)
            
            # all_bert_features_list = [self.t2s_model.model.bert_proj(item.to(self.t2s_model.device).transpose(0, 1)) for item in all_bert_features_list]
            # all_bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) for item in all_bert_features_list]
            # all_bert_features_batch = torch.stack(all_bert_features_list, dim=0)
            
            batch = {
                "phones": phones_batch,
                "phones_len": torch.LongTensor(phones_len_list).to(device),
                "all_phones": all_phones_batch,
                "all_phones_len": torch.LongTensor(all_phones_len_list).to(device),
                "all_bert_features": all_bert_features_batch,
                "norm_text": norm_text_batch
            }
            _data.append(batch)
        
        return _data, batch_index_list
    
    def recovery_order(self, data:list, batch_index_list:list)->list:
        '''
        Recovery the order of the audio according to the batch_index_list.
        
        Args:
            data (List[list(np.ndarray)]): the out of order audio .
            batch_index_list (List[list[int]]): the batch index list.
        
        Returns:
            list (List[np.ndarray]): the data in the original order.
        '''
        lenght = len(sum(batch_index_list, []))
        _data = [None]*lenght
        for i, index_list in enumerate(batch_index_list):
            for j, index in enumerate(index_list):
                _data[index] = data[i][j]
        return _data

    def stop(self,):
        '''
        Stop the inference process.
        '''
        self.stop_flag = True
        
    
    def run(self, inputs:dict):
        """
        Text to speech inference.
        
        Args:
            inputs (dict): 
                {
                    "text": "",               # str. text to be synthesized
                    "text_lang: "",           # str. language of the text to be synthesized
                    "ref_audio_path": "",     # str. reference audio path
                    "prompt_text": "",        # str. prompt text for the reference audio
                    "prompt_lang": "",        # str. language of the prompt text for the reference audio
                    "top_k": 5,               # int. top k sampling
                    "top_p": 1,               # float. top p sampling
                    "temperature": 1,         # float. temperature for sampling
                    "text_split_method": "",  # str. text split method, see text_segmentaion_method.py for details.
                    "batch_size": 1,          # int. batch size for inference
                    "batch_threshold": 0.75,  # float. threshold for batch splitting.
                    "split_bucket: True,      # bool. whether to split the batch into multiple buckets.
                    "return_fragment": False, # bool. step by step return the audio fragment.
                    "speed_factor":1.0,       # float. control the speed of the synthesized audio.
                    "fragment_interval":0.3,  # float. to control the interval of the audio fragment.
                    "seed": -1,               # int. random seed for reproducibility.
                }
        returns:
            tulpe[int, np.ndarray]: sampling rate and audio data.
        """
        ########## variables initialization ###########
        self.stop_flag:bool = False
        text:str = inputs.get("text", "")
        text_lang:str = inputs.get("text_lang", "")
        ref_audio_path:str = inputs.get("ref_audio_path", "")
        prompt_text:str = inputs.get("prompt_text", "")
        prompt_lang:str = inputs.get("prompt_lang", "")
        top_k:int = inputs.get("top_k", 5)
        top_p:float = inputs.get("top_p", 1)
        temperature:float = inputs.get("temperature", 1)
        text_split_method:str = inputs.get("text_split_method", "")
        batch_size = inputs.get("batch_size", 1)
        batch_threshold = inputs.get("batch_threshold", 0.75)
        speed_factor = inputs.get("speed_factor", 1.0)
        split_bucket = inputs.get("split_bucket", True)
        return_fragment = inputs.get("return_fragment", False)
        fragment_interval = inputs.get("fragment_interval", 0.3)
        seed = inputs.get("seed", -1)
        seed = -1 if seed in ["", None] else seed
        set_seed(seed)
        
        if return_fragment:
            # split_bucket = False
            print(i18n("分段返回模式已开启"))
            if split_bucket:
                split_bucket = False
                print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
            
        if split_bucket:
            print(i18n("分桶处理模式已开启"))
        
        if fragment_interval<0.01:
            fragment_interval = 0.01
            print(i18n("分段间隔过小，已自动设置为0.01"))
    
        no_prompt_text = False
        if prompt_text in [None, ""]:
            no_prompt_text = True
        
        assert text_lang in self.configs.langauges
        if not no_prompt_text:
            assert prompt_lang in self.configs.langauges

        if ref_audio_path in [None, ""] and \
            ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] is None)):
            raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")


        ###### setting reference audio and prompt text preprocessing ########
        t0 = ttime()
        if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
                self.set_ref_audio(ref_audio_path)

        if not no_prompt_text:
            prompt_text = prompt_text.strip("\n")
            if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
            print(i18n("实际输入的参考文本:"), prompt_text)
            if self.prompt_cache["prompt_text"] != prompt_text:
                self.prompt_cache["prompt_text"] = prompt_text
                self.prompt_cache["prompt_lang"] = prompt_lang
                phones, bert_features, norm_text = \
                    self.text_preprocessor.segment_and_extract_feature_for_text(
                                                                        prompt_text, 
                                                                        prompt_lang)
                self.prompt_cache["phones"] = phones
                self.prompt_cache["bert_features"] = bert_features
                self.prompt_cache["norm_text"] = norm_text
                
        
        ###### text preprocessing ########
        t1 = ttime()
        data:list = None
        if not return_fragment:
            data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
            if len(data) == 0:
                yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
                                                            dtype=np.int16)
                return
            
            batch_index_list:list = None
            data, batch_index_list = self.to_batch(data, 
                                prompt_data=self.prompt_cache if not no_prompt_text else None, 
                                batch_size=batch_size, 
                                threshold=batch_threshold,
                                split_bucket=split_bucket,
                                device=self.configs.device,
                                precison=self.precison
                                )
        else:
            print(i18n("############ 切分文本 ############"))
            texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
            data = []
            for i in range(len(texts)):
                if i%batch_size == 0:
                    data.append([])
                data[-1].append(texts[i])
                
            def make_batch(batch_texts):
                batch_data = []
                print(i18n("############ 提取文本Bert特征 ############"))
                for text in tqdm(batch_texts):
                    phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
                    if phones is None:
                        continue
                    res={
                        "phones": phones,
                        "bert_features": bert_features,
                        "norm_text": norm_text,
                    }
                    batch_data.append(res)
                if len(batch_data) == 0:
                    return None
                batch, _ = self.to_batch(batch_data, 
                            prompt_data=self.prompt_cache if not no_prompt_text else None, 
                            batch_size=batch_size, 
                            threshold=batch_threshold,
                            split_bucket=False,
                            device=self.configs.device,
                            precison=self.precison
                            )
                return batch[0]
            
        t2 = ttime()
        try:
            print("############ 推理 ############")
            ###### inference ######
            t_34 = 0.0
            t_45 = 0.0
            audio = []
            for item in data:
                t3 = ttime()
                if return_fragment:
                    item = make_batch(item)
                    if item is None:
                        continue
                    
                batch_phones:List[torch.LongTensor] = item["phones"]
                batch_phones_len:torch.LongTensor = item["phones_len"]
                all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
                all_phoneme_lens:torch.LongTensor  = item["all_phones_len"]
                all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
                norm_text:str = item["norm_text"]
        
                print(i18n("前端处理后的文本(每句):"), norm_text)
                if no_prompt_text :
                    prompt = None
                else:
                    prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
                
                with torch.no_grad():
                    pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
                        all_phoneme_ids,
                        all_phoneme_lens,
                        prompt,
                        all_bert_features,
                        # prompt_phone_len=ph_offset,
                        top_k=top_k,
                        top_p=top_p,
                        temperature=temperature,
                        early_stop_num=self.configs.hz * self.configs.max_sec,
                    )
                t4 = ttime()
                t_34 += t4 - t3
                
                refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\
                                                    .to(dtype=self.precison, device=self.configs.device)
                    
                batch_audio_fragment = []

                # ## vits并行推理 method 1
                # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
                # pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
                # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
                # max_len = 0
                # for i in range(0, len(batch_phones)):
                #     max_len = max(max_len, batch_phones[i].shape[-1])
                # batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
                # batch_phones = batch_phones.to(self.configs.device)
                # batch_audio_fragment = (self.vits_model.batched_decode(
                #         pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc
                #     ))
                
                # ## vits并行推理 method 2
                pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
                upsample_rate = math.prod(self.vits_model.upsample_rates)
                audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
                audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
                all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
                _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
                _batch_audio_fragment = (self.vits_model.decode(
                        all_pred_semantic, _batch_phones,refer_audio_spepc
                    ).detach()[0, 0, :])
                audio_frag_end_idx.insert(0, 0)
                batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
                    

                # ## vits串行推理
                # for i, idx in enumerate(idx_list):
                #     phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
                #     _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0))   # .unsqueeze(0)#mq要多unsqueeze一次
                #     audio_fragment =(self.vits_model.decode(
                #             _pred_semantic, phones, refer_audio_spepc
                #         ).detach()[0, 0, :])
                #     batch_audio_fragment.append(
                #         audio_fragment
                #     )  ###试试重建不带上prompt部分
                
                t5 = ttime()
                t_45 += t5 - t4
                if return_fragment:
                    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
                    yield self.audio_postprocess([batch_audio_fragment], 
                                                    self.configs.sampling_rate, 
                                                    None, 
                                                    speed_factor, 
                                                    False,
                                                    fragment_interval
                                                    )
                else:
                    audio.append(batch_audio_fragment)
                    
                if self.stop_flag:
                    yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
                                                            dtype=np.int16)
                    return

            if not return_fragment:
                print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
                yield self.audio_postprocess(audio, 
                                                self.configs.sampling_rate, 
                                                batch_index_list, 
                                                speed_factor, 
                                                split_bucket,
                                                fragment_interval
                                                )         
        except Exception as e:
            traceback.print_exc()
            # 必须返回一个空音频, 否则会导致显存不释放。
            yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
                                                            dtype=np.int16)
            # 重置模型, 否则会导致显存释放不完全。
            del self.t2s_model
            del self.vits_model
            self.t2s_model = None
            self.vits_model = None
            self.init_t2s_weights(self.configs.t2s_weights_path)
            self.init_vits_weights(self.configs.vits_weights_path)
        finally:
            self.empty_cache()
    
    def empty_cache(self):
        try:    
            if "cuda" in str(self.configs.device):
                torch.cuda.empty_cache()
            elif str(self.configs.device) == "mps":
                torch.mps.empty_cache()
        except:
            pass 
        
    def audio_postprocess(self, 
                          audio:List[torch.Tensor], 
                          sr:int, 
                          batch_index_list:list=None, 
                          speed_factor:float=1.0, 
                          split_bucket:bool=True,
                          fragment_interval:float=0.3
                          )->tuple[int, np.ndarray]:
        zero_wav = torch.zeros(
                        int(self.configs.sampling_rate * fragment_interval),
                        dtype=self.precison,
                        device=self.configs.device
                    )
        
        for i, batch in enumerate(audio):
            for j, audio_fragment in enumerate(batch):
                max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
                if max_audio>1: audio_fragment/=max_audio
                audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
                audio[i][j] = audio_fragment.cpu().numpy()
            
        
        if split_bucket:
            audio = self.recovery_order(audio, batch_index_list)
        else:
            # audio = [item for batch in audio for item in batch]
            audio = sum(audio, [])
            
            
        audio = np.concatenate(audio, 0)
        audio = (audio * 32768).astype(np.int16) 
        
        try:
            if speed_factor != 1.0:
                audio = speed_change(audio, speed=speed_factor, sr=int(sr))
        except Exception as e:
            print(f"Failed to change speed of audio: \n{e}")
        
        return sr, audio
            
        
        
       
def speed_change(input_audio:np.ndarray, speed:float, sr:int):
    # 将 NumPy 数组转换为原始 PCM 流
    raw_audio = input_audio.astype(np.int16).tobytes()

    # 设置 ffmpeg 输入流
    input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)

    # 变速处理
    output_stream = input_stream.filter('atempo', speed)

    # 输出流到管道
    out, _ = (
        output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
        .run(input=raw_audio, capture_stdout=True, capture_stderr=True)
    )

    # 将管道输出解码为 NumPy 数组
    processed_audio = np.frombuffer(out, np.int16)

    return processed_audio